This study estimates the benefits and costs of a free clinic
providing primary care services. Using matched data from a free clinic
and its corresponding regional hospital on a sample of newly enrolled
clinic patients, patients' non-urgent emergency department (ED) and
inpatient hospital costs in the year prior to clinic enrollment were
compared to those in the year following enrollment to obtain financial
benefits. We compare these to annual estimates of the costs associated
with the delivery of primary care to these patients. For our sample
(n=207), the annual non-urgent ED and inpatient costs at the hospital
fell by $170 per patient after clinic enrollment. However, the cost
associated with delivering primary care in the first year after clinic
enrollment cost $505 per patient. The presence of a free primary care
clinic reduces hospital costs associated with non-urgent ED use and
inpatient care. These reductions in costs need to be sustained for at
least 3 years to offset the costs associated with the initially high
diagnostic and treatment costs involved in the delivery of primary care
to an uninsured

It has been estimated that the annual cost of uncompensated health
services provided to uninsured people in the United States was $56
billion in 2008 (Hadley, Holahan, Coughlin, & Miller, 2008). In
particular, the utilization and costs of emergency departments (ED) for
non-urgent care has been a considerable and growing problem over the
last several decades (Baker & Baker, 1994; Grumbach, Keane &
Bindman, 1993). More recently, there is evidence that potentially
preventable hospitalizations are on the rise as well (Russo, Jiang &
Barrett, 2007). In response to this inefficient and costly utilization
of hospital care across the country, many hospitals and communities have
sought different mechanisms for providing primary care for the uninsured
or under-insured. These mechanisms have ranged from free-standing
clinics, to mobile units, to expansion of primary care services provided
in already established settings, such as schools or churches. Funding
for these initiatives, which provide both free or nearly free healthcare
in many cases, comes from federal, state and local governments,
faith-based organizations, non-profit groups, businesses, hospitals, and
individual donations.

Most policy makers, healthcare industry leaders, funders and
providers of primary healthcare believe that these primary care clinics
are "cost-effective" because access to preventive care is
believed to be a more efficient use of scarce health resources relative
to the use of ED or inpatient services that might eventually be needed
for untreated health conditions. However, there are surprisingly few
studies that have rigorously assessed the economic impact of the
provision of free primary care within a community.

Several studies have evaluated the utilization of ED services post
implementation of primary care provision. A study conducted by Zahradnik
(2008) found that the provision of free or low-cost primary care to
uninsured patients in Michigan resulted in per person reductions in the
use of hospital EDs from 0.33 to 0.031 visits per month. In Kansas,
after a policy was applied to provide primary care to medically
underserved people, researchers found that ED visits by the uninsured
declined 39% over the subsequent two years (Smith-Campbell, 2000). In
another study, researchers estimated that a program that provides
indigent patients with free primary care decreased ED utilization from
1.89 to 0.83 visits per year, and decreased charges for ED care by $457
per person (Davidson, Giancola, Gast, Ho, & Waddell, 2003). In
Georgia, a study found that rural counties without a community health
center (CHC) primary care clinic site had 33% higher rates of uninsured
ED visits per 10,000 uninsured people than rural counties with a CHC
(Rust et al., 2009). Finally, Young, D'Angelo, and Davis (2001)
found that operating an inschool health center to provide primary care
resulted in a significant decrease (p<0.03) in non-urgent ED use for
the student population, from 44 visits to 26 visits per year for a
sample of 216 students.

There is also evidence indicating that preventable hospitalization
rates reflect inadequate ambulatory care. Parchman and Culler (1994)
analyze hospital discharge data in Pennsylvania and find that areas with
higher per capita rates of general practice physicians have lower rates
of ambulatory care sensitive hospitalization, that is, hospitalizations
that can be prevented with effective primary care which either prevents
the onset of an illness or controls acute episodes of the disease.

Despite this evidence that the provision of primary care may lower
utilization and costs of ED and inpatient services, there is little
evidence to establish whether there are financial returns for investing
in primary care. Therefore, the purpose of this study is to estimate
whether the financial benefits of reducing the use and costs of hospital
care outweigh the costs of investing in primary care.

To our knowledge, there is only one study that has examined the
returns on investment of the provision of primary care. Oriol et al.
(2009) examined the returns on investment of a mobile healthcare unit
providing primary care and found that the community saved $36 for every
$1 invested in the mobile healthcare unit. Oriol et al. (2009) compute
the savings by multiplying the state average per visit preventable ED
cost with the number of preventable ED visits they assume were avoided.
To do this calculation, they have to make the strong assumption that if
the sample population did not have access to the mobile van, 80% of van
visits would have resulted in utilization of ED care. The strategy of
this study differs from that employed by Oriol et al. (2009) in that we
are able to estimate the change in non-urgent ED and inpatient hospital
costs directly by comparing actual one-year prior to clinic enrollment
costs with one-year post-enrollment costs. Moreover, we have all
hospital costs so we can also estimate the savings due to the prevention
of hospitalizations, as well as more accurately estimate the costs of
primary care delivery by including other hospital costs, which are a
potentially important part of the cost associated with the delivery of
primary care. As a result, this study will provide much more accurate
and comprehensive estimates of the costs and benefits of providing
primary care in a free clinic setting.

Data

Data for this analysis come from a large free clinic in northern
Georgia. The clinic offers free medical care to the indigent, homeless
and low-income people of their community, who have no health insurance
and who cannot afford medical care. Hospital data come from a large
regional hospital serving the same community as the clinic. Clinic data
collection. The new clinic patient sample was chosen based on clinic
enrollment date. All persons attending the clinic as a first-time
patient between January 1st, 2006 and June 29th, 2007, were considered
for inclusion in this study. Of the 289 patients identified from the
clinic population, 18 clinic patients could not be linked to the
hospital database as ever having received services, and therefore were
excluded from consideration in this study. An additional 5 patients were
dropped from this study because their clinic files could not be located,
and 56 patients were dropped because they did not receive any clinic
services in the one-year post-clinic enrollment (and therefore, the
benefits of clinic enrollment may not have been realized). An additional
3 patients were dropped because of inpatient hospital admission
length-of-stays that were outside of the 95% confidence interval for the
sample (20 days, 40 days, and 49 days, respectively), and thus potential
outliers. Therefore, the final sample size included in this study is
207. Clinic costs come from the 2007 clinic financial report.

Hospital data collection. Clinic patients were identified in the
hospital's electronic medical records system based on the following
clinic population identifiers: name, date of birth, gender, clinic
enrollment date, and social security number (when available). Based on
clinic enrollment data, all hospital data available for the clinic
population were abstracted from the electronic medical records system
for the one-year pre-clinic enrollment date to one-year post-clinic
enrollment date. Hospital data categories for each patient included the
following: ED services and their associated costs subcategorized as
urgent or non-urgent, the total cost for inpatient visits, and the total
cost of all other hospital services. Other hospital services included:
ancillary care, diabetes education, imaging center services, hospice
care, lab pathology, mental health services, mobile mammogram,
outpatient chemotherapy, radiation oncology, outpatient rehabilitation,
workers compensation rehabilitation, short-stay surgery, and wound
repair. The total cost of a hospital service is defined as the sum of
the variable cost, direct fixed cost, and overhead cost. All cost values
were converted to 2009 U.S. dollars using the hospital and related
services component of the Consumer Price Index (Bureau of Labor
Statistics, 2009).

METHODS

The purpose of this study is to compare the costs associated with
providing primary care to the population under consideration with the
savings to the hospital that result from having a free clinic in the
region. The time frame for assessing costs and benefits was one year.
The savings we include are derived from the decreased utilization of
non-urgent ED care and inpatient hospital care, because clinic visits
may be substitutes for non-urgent ED visits and may prevent
hospitalization as discussed in the introduction. The primary care costs
we include are the costs for clinic services, not including the value of
volunteered services, and the costs of diagnostic and other hospital
services associated with the delivery of that primary care. We add other
hospital services to the cost side of the equation because we
hypothesize that initial access to primary care for an uninsured
population will involve the need for a variety of tests and services for
their backlog of previously undiagnosed and untreated conditions.

Benefits

We estimated hospital savings from the presence of the free clinic
using a two-part model (Duan, Manning, Morris, & Newhouse, 1983;
Cameron & Trivedi, 2005), which estimates separately the use of a
hospital category (e.g. inpatient care) and the resulting costs
conditional on any use. Specifically, we model the patient's
decision to have a specific type of hospital service using a probit
model. Then, we estimate the costs of the service using ordinary least
squares (OLS) for the sample with non-zero costs. In both models, the
regressors include patient's age, gender, race and ethnicity, a
list of five conditions the patients have at enrollment (hypertension,
diabetes, arthritis, depression, and/or asthma), and a post-enrollment
indicator. (1) The expected hospital expense for each individual is
estimated by multiplying the predicted value of the probability of any
expense from the first regression and the predicted value of the
expense.

The two-part model is used because hospital costs have an extremely
skewed distribution, which make estimating with a simpler model
inappropriate especially with a small sample. In particular, the
frequency of some types of hospital care is small leading to a large
number of zeros in the distribution, and the costs of care vary
substantially such that the maximum expenditures can be high. This
method also allows us to estimate the costs controlling for a variety of
individual characteristics, such that the results are slightly more
generalizable. We use this model to estimate the one-year costs
pre-enrollment and one-year costs post-enrollment for non-urgent ED care
and inpatient care. We can test for statistical differences between the
pre- and post-enrollment costs using a standard t-test.

Costs

We estimate costs for the care of the 207 patients from the clinic
by taking the average cost per visit from the financial report and
multiplying that figure by the total number of visits incurred by the
sample patients. We add to this figure the difference between the costs
pre-enrollment and the costs post-enrollment for other hospital
services. We estimated these hospital costs using the two-part model as
described above. (2)

RESULTS

Table 1 provides some basic summary statistics on the patient
population. The majority of patients are female, non-elderly adults. The
clinic sees few children and seniors because most in these age groups
are eligible for Medicaid or Medicare making them ineligible for
services at the clinic. There is a large Hispanic clinic population,
which is reflective of the community demographics. We can also observe
whether a patient has certain health conditions from the clinic
enrollment records. Thirty-five percent of clinic patients have
hypertension, nearly a quarter has diabetes, and 11% has depression.

From the matched hospital records, we estimate that the costs
associated with non-urgent ED visits declined by 25% and inpatient care
costs declined by 15% (see Table 2). Both differences are statistically
significant (p<0.01). The savings totals $35,146 for the 207 patients
or $170 per patient.

From the clinic's 2007 financial report, we estimate that the
average cost per visit is $54.44. The 207 new enrollees had 1,224 clinic
visits in their first year. Thus, the total clinic expense for these 207
patients is $66,635, or $322 per patient. In addition, the hospital
costs associated with the delivery of primary care to this population
increased from $33,109 in the pre-enrollment period to $71,066 after
clinic enrollment and represents a statistically significant increase.
This amounts to an additional $37,957 in costs, or $183 per patient.

DISCUSSION

We find that, for a free clinic providing primary care services,
the costs of non-urgent ED care and inpatient care decreased
significantly post-clinic enrollment, by an estimated $170 per patient.
However, there are important costs associated with that reduction that
cannot be ignored: they include the costs of the clinic themselves and
the costs of other hospital services for testing/diagnostics associated
with that primary care, which total $505 per patient. Assuming that the
high number of clinic visits and the increase in other hospital services
is a temporary cost that falls after diagnosis and the initiation of
disease management, the hospital savings per year must be sustained for
at least 3 years to offset these initially high costs after enrollment.

Consistent with the literature, we find that the free clinic
reduces non-urgent ED costs but our estimate of the net benefit is lower
than that found by others (Oriol et al., 2009). This is due in part to
the frequency of clinic visits needed to offset a non-urgent ED care
visit. On the other hand, we find that the majority of the costs savings
associated with clinic enrollment is derived from a reduction in the
length of inpatient hospital stays.

There are several study design and data limitations to note. First,
our study only included the utilization of hospital and clinic services
for one-year post clinic enrollment. Thus, we must hypothesize about the
future savings and costs associated with access to primary care
services. We argue that a majority of savings may come in future years
beyond the one-year post-clinic enrollment since the first year involves
costly diagnosis and initiation of disease management.

Second, our benefit estimates do not include the health benefits
resulting in improvements in health-related quality of life for the
individual patients or their potential gains in productivity due to
improved health. Further, our cost estimates do not include the value of
volunteered services, which were not included in the clinic expense
figures given above. Similarly, because we collected hospital costs
directly from the hospital, we assume that the only physician costs that
are included are from those physicians that are employed by the
hospital. The costs associated with physicians not employed by the
hospital, e.g. specialty care, are likely not included in the total
hospital costs. Therefore our cost estimates are an underestimate from
the societal perspective (where all costs would be included regardless
to whom they accrue), but an accurate estimate from the free clinic and
the hospital perspectives.

Third, if one considers enrollment in the free clinic as "the
intervention," a more advanced evaluation analysis would include a
control group that does not receive the intervention for comparison
purposes. We were not able to conduct that type of study because we were
analyzing data from persons who had already received the intervention
(i.e., they had already enrolled in the clinic). Therefore, we could not
make any random assignments to intervention or control retrospectively.
Although it may have been possible to match to a control group not
enrolled in the clinic, e.g., by analyzing healthcare utilization data
for a similar population matched on age, gender, income, etc., these
data were not available to us in the community. Thus, the pre-post
design was the best method available for this analysis.

Fourth, there were 18 clinic patients dropped from our study that
could not be matched to the hospital's electronic medical record
database as ever having received services. For these patients, it is
possible that 1) they did receive hospital services but could not be
matched because of documentation status or other administrative issues,
2) they died or moved and therefore did not have continuous service
within the community, 3) they received services in another hospital
setting outside the community, or 4) they legitimately did not receive
any hospital services. If the latter is correct and they should have
been included in the study, this would impact our total sample size and
decrease our cost per case estimates (although it would not impact the
pre/post changes). For any of the other scenarios, it was not possible
to correct for the bias that their exclusion may have caused. This
potential bias speaks to one of the many challenges in doing research
that attempts to capture all of an individual's healthcare costs
within a fragmented healthcare delivery system.

Finally, 56 patients were excluded from this study because they did
not receive any clinic services post-enrollment. These cases were
dropped because we thought the benefits of clinic care could not be
realized in one visit only. While these limitations may affect the
results, they are such that the estimates reported are as conservative
as possible in terms of estimated benefits compared to costs.

CONCLUSION

Recent health care reform legislation will greatly increase the
number of previously uninsured people seeking access to primary care
services. There will also continue to be a large number of immigrants
and others without access to insurance. As such, communities, hospitals,
and other healthcare providers will need to continue exploring new
mechanisms for providing primary care services to vulnerable
populations. The results of this study suggest that access to primary
care will reduce the inefficient use of ED resources for non-urgent care
and will reduce costly hospitalizations even in the short-run. However,
as many are predicting, there will be initial costs associated with
access to primary care for an uninsured population with an accumulation
of health problems that have been undiagnosed and untreated.

(1) We conducted a Chow test to determine whether we could estimate
the pre-enrollment and post-enrollment data in the same regression to
increase precision and found that both periods could be combined for
both non-urgent ED care and inpatient care costs.

(2) A Chow test indicated that we could not combine the
pre-enrollment and post-enrollment data in the same regression in the
case of other hospital costs. Thus, the pre and post costs were
estimated separately and the indicator for post-enrollment was not used
in these regressions.